Does Financial Permeation Promote Economic Growth? Some Econometric Evidence From Asian Countries

Abstract

This study endeavors to explore whether financial permeation stimulates economic growth in Asian region. To answer this, we collect data of 24 Asian economies for the duration of 2004 to 2016 and apply panel unit root, Granger causality, and regression techniques. The regression results controlled for country and time effects reveal that various indicators of financial permeation have substantial positive impact on the economic growth of Asian economies. Based on the findings of Granger causality, we find that financial permeation as well as economic openness has mutual causalities with economic growth. Therefore, it seems rational to conclude that financial permeation has positive impact on the economic growth in Asian economies. We also find a negative impact of financial crisis (2007-2008) on economic growth of Asian countries.

Keywords

financial permeation economic growth economic openness financial crisis

Introduction

One of the main objectives of every economy is to accelerate and sustain high economic growth and financial permeation can play a significant role to achieve this objective. Recently, the idea of financial permeation has gained an increased consideration and as such has been accepted as a policy primacy by central banks, supervisory body, and Governments’ officials. During the G20 summit 2010 held in Seoul (Korea), financial permeation has been accepted as one of the major pillars of world development agenda (Zins & Weill, 2016). The reasons for this recent emphasis are twofold: First, financial permeation aims to ensure access by providing financial services to the people and get them under the canopy of formal financial system so that these people may play their role in economic activity (Sethi & Acharya, 2018). Second, it targets poor and disadvantaged people in specific, and therefore it may reduce poverty and increase economic growth (Cull, Beck, & Holle, 2014). In short, the overriding goal of financial permeation is to build an atmosphere in which all the individuals may have an easy and equal access to financial services and to warrant contribution of all the individuals in the economic activity.

Recent literature regarding financial permeation focusing on different regions of the globe enhances our understanding of its impact on economic growth. For example, Kim, Yu, and Hassan (2018) studied the impact of financial permeation in Organisation of Islamic Cooperation (OIC) countries and find a positive influence of financial permeation on economic growth. Pradhan, Arvin, Norman, Nair, and Hall (2016) studied the same for ASEAN (Association of Southeast Asian Nations) states, whereas Inoue and Hamori (2016) inspected the effects of financial permeation for sub-Saharan African countries. A recent study investigated the effects of financial permeation in 31 advanced and emerging economies and found a significant affirmative association of financial permeation with economic growth (Sethi & Acharya, 2018). However, findings of these studies are mixed; some found positive impacts, whereas some observed negative effects and thus inconclusive. In addition, none of the previous significant studies comprehensively examined the role of financial permeation in economic growth of Asian region as a whole. Asian economies have experienced a higher economic growth in recent years, especially when compared with the economies outside of the Asian region. It is the reason that Asia has become a vital part of the global economy and as a consequence the center of global economic gravity has moved to Asia. Many countries in Asia have paid attention to promote and ensure inclusive growth and accordingly formulated financial inclusion strategies to achieve the same by increasing the level of financial inclusion¹ (Beck & Ayyagari, 2015). However, the extent to which financial permeation affected the economic growth of the Asian region is yet to be explored. Consequently, the objective of this study is to address these significant research gaps by investigating the impact of financial permeation on economic growth of Asian economies and thus provide a conclusive direction of the impacts.

To achieve the objective of the study, we collect data for 24 Asian economies for the period from 2004 to 2016 and panel unit root, Granger causality, and regression techniques controlled for country and time effects. The contribution of this study is manifold: First, the study investigates the impact of financial permeation on economic growth of 24 Asian countries; second, the study also investigates the impact of economic openness on economic growth of Asian countries; third, the study considers two dimensions of financial permeation (supply-led, demand-driven) and use two proxies for each of the dimensions. Moreover, the study also controls for global financial crisis (2007-2008) and investigates the impact of financial permeation on economic growth.

The rest of the article is structured as follows; Section “Literature Review” summarizes significant literature regarding the study, Section “Methodology” explains data and methodology; Section “Empirical Findings and Discussion” explains the results; and Section “Conclusion” concludes the results. References are provided at the end.

Literature Review

Notion of “inclusive finance” has been considered as a precedence program by organizations including the World Bank, Asian Development Bank, G20, governments, and policymakers of developing countries. Hannig and Jansen (2010) discussed that financial inclusion programs are purported to provide easier access to appropriate financial services to individuals, including marginalized section of the society that leads to contribute to a country’s economic development. Sarma (2015) delineated financial inclusion as a route to ensure easy entrance, availability, and use of financial services for all individuals of an economy. This elucidation firmly focuses on access and usage dimension of proper financial services for whole segments of a country. Similar to Sarma (2015), we focus on access to as well as usage facet of financial inclusion and describe financial permeation as a procedure which targets to enlarge the financial system among individuals. Just as water permeates through the sand, a proliferation in the financial sector action widens added economic prospects among individuals including poor, women, and marginalized, which tend to stimulate economic growth. In developing countries, the banking sector is considered as the crucial pillar and the greatest role-playing sector in the process of economic development. Therefore, we measure two dimensions of financial permeation, that is, supply led and demand driven. Furthermore, we measure each dimension using two proxies. Financial access is measured by commercial bank branches per 100,000 adults, and commercial bank branches per 1,000 km². To measure financial depth, we take the proportion of outstanding deposits with commercial banks to gross domestic product (GDP) and the share of outstanding credits from commercial banks to GDP.

Previous studies inspected the effects of financial permeation in different ways. For example, applying financial access survey data on Uganda and Kenya, Johnson and Nino-Zarazua (2011) examined whether the countries’ socioeconomic and demographic elements have influences on access to finance. Demirgüç-Kunt and Klapper (2012) employed Global Findex and Enterprise Surveys data and explored the picture of access of individuals and small and medium-sized enterprises (SMEs) to financial services in African economies. They observed that in Africa most of the individuals use informal sources of finance and that most of the SMEs are also unwilling to go to banks. In the context of microfinance, Inoue and Hamori (2013) studied the influence of financial permeation on poverty percentage. Employing 90 developing countries’ panel data, from 1990 to 2008, they observed significant impacts of financial permeation on poverty reduction. Using the data of 37 sub-Saharan African countries for the period from 2004 to 2012, Inoue and Hamori (2016) tested the association between financial access and economic development of countries. They found that access to financial resources contributes to the process of economic growth of sample economies. Yang (2019) conducted a study on the issue of financial development and economic growth of middle-income countries. He found that the level of financial development significantly contributes to economic growth of the sample countries.

Mandel and Seydl (2016) conducted a research on U.S. banks’ data. They found a significant contribution of financial inclusion to financial stability which in order promotes economic stability of the country. Using insurance market penetration data of ASEAN countries for the period of 1988 to 2012, Pradhan et al. (2016) inspected the rapport of financial inclusion with economic growth. They observed positive impacts of insurance market penetration on economic growth of the sample countries. Employing data for the period of 2004 to 2016, Bigirimana and Hongyi (2018) found that financial inclusion through commercial banks has major long-run effects on Rwanda’s economic growth. Kim et al. (2018) employed the data of 55 OIC countries from 1990 to 2013 and found significant contributory effects of financial inclusion on economic growth.

Reviewing prior studies, we observe mixed evidences on the link between financial permeation and economic growth. We also observe that none of the previous studies investigated the impact of financial permeation on economic growth of the Asian region as a whole. Accordingly, to fill these gaps, we put forward a framework starting from the methodology, examine the issue regarding the impacts of financial permeation on economic growth of Asian economies, and contribute to the existing literature.

Method

Sample and Data Sources

To address the objective of our study, we collect data from 24 Asian economies. We have chosen these Asian economies as we confronted with scarcity of data, and therefore we had to reduce our sample to 24 countries. World Development Indicators (WDI) of World Bank is considered as the leading cross-country databank in this field. It comprises cross-country time series data on many variables related to financial permeation and development.² However, this databank encompasses comparatively data for a shorter period of time on main variables alongside many economy-wise missing data. Based on data handiness, we have accumulated and sorted data on economic growth (LogGDPC_i,t) from 2004 to 2016 and control variables of Asian economies from the World Bank database. The International Monetary Fund’s (IMF) Financial Access Survey (FAS) database has been used to collect data on financial permeation variables. FAS is an accessible cross-country database,³ which encompasses commercial banks and non-bank financial institutions’ time series data.

Variables

Dependent and explanatory variables

In this research, our main aim is to examine the link between financial permeation and economic growth. Therefore, following previous significant studies (Beck & Levine, 2004; Beck, Levine, & Loayza, 2000; Ehigiamusoe & Lean, 2018), economic growth is considered as a dependent variable, which we measure by real GDP per capita. As the target of this study is to inspect whether financial permeation contributes to countries’ economic growth, financial permeation is the central independent variable of the study. We consider four proxies to measure financial permeation based on maximum availability of data: number of commercial bank branches per 100,000 adults (LogFP1_i,t), number of commercial bank branches per 1,000 km² (LogFP2_i,t), proportion of outstanding deposits with commercial banks to GDP (LogFP3_i,t), and ratio of outstanding loans from commercial banks to GDP (LogFP4_i,t). The first two capture individual’s accessibility to financial services and are known as supply-led financial permeation. Access to finance is vital to a country’s economic growth, especially for developing nations, because access to finance from formal organizations generates economic prospects and consequently leads to the economic development of the country. Previous empirical studies applied these proxies and observed a positive connection between these two proxies and economic growth in India (Anand & Chhikara, 2013; Joseph & Varghese, 2014) and in Ghana, Nigeria, and South Africa (Ehigiamusoe & Lean, 2018). The demand-driven parts of financial permeation comprise two proxies which focus on the usage dimension of financial services by the individuals. Progress in accessibility to and usage of formal financial services expand opportunities for the households to pursue economic doings that ultimately leads to acceleration of economic growth. Employing these proxies, previous empirical studies examined significant positive effects on economic growth in Nigeria (Babajide, Adegboye, & Omankhanlen, 2015) Ghana, Nigeria, and South Africa (Ehigiamusoe & Lean, 2018) and India (Sharma, 2016). In line with these studies, we also expect an affirmative rapport between financial permeation proxies and economic growth.

Control variables

We have considered some control variables in our study to segregate the relationship between level of financial permeation and economic growth. First, consistent with previous studies (Ehigiamusoe & Lean, 2018; Inoue & Hamori, 2013), we control for the portion of net foreign direct investment (FDI) inflows to GDP (FDI_i,t), because the results of previous studies have revealed that FDI has substantial effects on countries’ economic growth (Ali & Mingque, 2018; Comes, Bunduchi, Vasile, & Stefan, 2018; Ehigiamusoe & Lean, 2018; Makki & Somwaru, 2004; Solarin & Dahalan, 2014; Zhuang, 2017). Arguing the same, we assume that FDI is important and thus controlled for in this study.

A country’s unemployment situation, the share of the labor force without having work but looking for employment, also influences its economic growth. Higher unemployment rate has a negative influence on countries’ economic growth (Imran, Mughal, Salman, & Makarevic, 2015; Kukaj, 2018). Recognizing its consequences, we control for the unemployment effect by adding this in the analysis, where it is being measured as the ratio of total labor force without work (UMP_i,t). Another control variable, education level, is a vital element of development and plays a crucial role in the process of human resource development that finally promotes economic growth (Ehigiamusoe & Lean, 2018; Gundlach, 2001; Seetanah, 2009). Thus, we measure it by the percentage of primary school enrollment to population (EDU_i,t) and expect encouraging effects of education levels on a countries’ economic growth. Furthermore, population is also an imperative factor of economic growth as previous studies (Akbar, Imdadullah, Ullah, & Aslam, 2011; Solarin & Dahalan, 2014) noticed negative influences of population growth on countries’ economic growth. Thus, we also believe that population (LPOPG_i,t) may influence the economic growth and it is important to control for population growth.

Furthermore, we control for inflation rate (LINF_i,t), which is measured by consumer price index (CPI), because many studies found that inflation has influences on countries’ economic growth (Adebola & Dahalan, 2011; Behera & Mishra, 2017; Divya & Devi, 2014; Mallik & Chowdhury, 2001). Another control variable we added is the ratio of domestic credit delivered by the financial sector to GDP. Abida, Sghaier, and Zghidi (2015) stated the significance and used this variable in their analysis. They observed significant effects of domestic credit on economic growth. Hence, we applied the ratio of domestic credit provided by the financial sector to GDP and thus control it in our research.

Moreover, our study period is from 2004 to 2016, and therefore it is important to control for the impact of global financial crisis of 2007-2008. Prochniak and Wasiak (2017) observed negative consequences of financial crisis on economic growth, whereas based on data of 26 European countries for the period of 1990 to 2016, Asteriou and Spanos (2019) found that financial crisis slows down the economic growth. Following Kodongo and Ojah (2016), we incorporate time dummies in this study. The global financial crisis dummy variable, GFC, takes the value of 1 for years 2007 and 2008, whereas it is 0 for the other years. Table 1 presents the variables, definitions, and measurement along with the sources of data.

Table 1.

Variables, Their Proxies, Model Name, and Source.

Variable	Measurement	Legend	Sources
Economic growth	Log of per capita real GDP	LogGDPC_i,t	WDI
Financial permeation	Number of commercial bank branches per 100,000 adults	LogFP1_i,t	FAS
	Number of Commercial bank branches per 1,000 km²	LogFP2_i,t	FAS
	Outstanding deposits with commercial banks (percentage of GDP)	LogFP3_i,t	FAS
	Outstanding loans from commercial banks (percentage of GDP)	LogFP4_i,t	FAS
Control variables	Share of net FDI inflows to GDP	FDI_i,t	WDI
	Unemployment rate (proportion of total labor force without work)	UMP_i,t	WDI
	Proportion of primary school enrollment rate	EDU_i,t	WDI
	Inflation rate of CPI (percentage)	LINF_i,t	WDI
	Population growth rate	LPOPG_i,t	WDI
	Domestic credit provided by the financial sector (percentage of GDP)	DCPFS_i,t	WDI
Financial crisis	Dummy 1 for 2007 and 2008; 0 otherwise	GFC_t

Note. GDP = gross domestic product; WDI = World Development Indicators; FAS = Financial Access Survey; FDI = foreign direct investment; CPI = consumer price index.

The Model

To empirically explore the impact of financial permeation on economic growth, and based on our discussion in Section “Variables,” we develop the following regression equation⁴:

{LogGDPC}_{i, t} = C + β_{1} X_{i, t - 1} + β_{2} Y_{i, t - 1} + {CNT}_{i} + {YR}_{t} + ε_{i, t},

(1)

where LogGDPC_i,t is the dependent variable which mirrors the economic growth of countries i at time t; X_i_{,t – 1} is a 1-year lag of financial permeation measured by four proxies, that is, LogFP1_{i,t – 1}, LogFP2_{i,t – 1}, LogFP3_{i,t – 1}, LogFP4_{i,t – 1}; β₁ is the coefficient, which computes the effects of financial permeation on economic growth; Y_i_{,t – 1} is a 1-year lag of control variables, that is, FDI_{i,t – 1} (fraction of net FDI inflows to GDP); UMP_{i,t – 1} (unemployment rate); EDU_{i,t – 1} (ratio of primary school enrollment rate); LPOPG_{i,t – 1} (population growth rate); LINF_{i,t – 1} (inflation rate); DCPFS_{i,t – 1} (domestic credit provided by the financial sector); β₂ is the coefficient for control variables; CNT_i is the country fixed effect, YR_t is the time fixed effect, and ε_i,t is the error term.

To incorporate the impact of global financial crisis, we include a dummy variable in the regression equation (Equation 1) and develop the following:

{LogGDPC}_{i, t} = C + β_{1} X_{i, t - 1} + β_{2} Y_{i, t - 1} + β_{3} {GFC}_{t} + {CNT}_{i} + ε_{i, t},

(2)

where GFC_t is a time dummy that takes 1 for years 2007 and 2008, and 0 otherwise; β₃ is the coefficient explaining the impacts of global financial crisis on countries’ economic growth. Furthermore, we employ country and time fixed effects regression in Equation 1 and country fixed effects regression in Equation 2 considering the findings of Hausman test, our dataset type (Baltagi, 2008), and a recent significant study (Sethi & Acharya, 2018).

Empirical Findings and Discussion

Descriptive Statistics

Table 2 reveals the descriptive statistics for the complete dataset and Table 3 provides mean values for all the variables, separately for all the countries included in our sample. The mean value of 3.287 with a standard deviation of 0.352 for LogGDPC_i,t explains that these Asian countries have enjoyed a stable economic growth during 2004 to 2016. Afghanistan has the lowest economic growth of 2.711 and Malaysia has the highest economic growth of 3.966. Afghanistan also has the lowest number of commercial bank branches per 100,000 adults with a mean value of 0.186 for LogFP1_i,t and Mongolia has the highest with a mean value of 1.761. On the contrary, Maldives has the highest number of commercial bank branches per 1,000 km² with a mean value of 2.015. Furthermore, the mean value of 1.636 with a median of 1.625 for LogFP3_i,t explains that the ratio of outstanding deposits with commercial banks to GDP of half of the countries is close to the mean value of overall dataset and China has the highest with a mean value of 2.121. The mean value of 1.505 with a median of 1.567 for LogFP4_i,t explains that the ratio of outstanding loans from commercial banks to GDP of half of the countries is higher than the mean value of overall dataset and Malaysia has the highest with a mean value of 2.007.

Table 2.

Summary Statistics.

Variable	Observations	M	Median	SD	Minimum	Maximum
LogGDPC_i,t	312	3.287	3.306	0.352	2.561	4.043
LogFP1_i,t	312	0.912	0.944	0.377	−0.428	1.855
LogFP2_i,t	312	0.727	0.857	0.676	−1.143	2.176
LogFP3_i,t	312	1.636	1.625	0.266	0.556	2.196
LogFP4_i,t	312	1.505	1.567	0.356	0.065	2.078
FDI_i,t	311	3.954	2.711	5.312	−37.166	43.912
UMP_i,t	312	3.723	3.535	2.302	0.160	10.001
EDU_i,t	312	108.111	107.376	9.871	83.070	145.128
LINF_i,t	312	0.663	0.739	0.378	−1.452	1.544
LPOPG_i,t	312	0.107	0.138	0.240	−0.675	0.650
DCPFS_i,t	312	57.611	49.371	43.789	−57.298	215.026

Table 3.

Country-Wise Mean Value for All the Variables.

Country	LogGDPC_i,t	LogFP1_i,t	LogFP2_i,t	LogFP3_i,t	LogFP4_i,t	FDI_i,t	UMP_i,t	EDU_i,t	LINF_i,t	LPOPG_i,t	DCPFS_i,t
Afghanistan	2.711	0.186	−0.441	1.153	0.663	1.366	8.434	103.870	0.808	0.490	0.133
Bangladesh	2.883	0.875	1.775	1.657	1.557	1.116	4.149	106.432	0.861	0.084	55.023
Bhutan	3.317	1.151	0.268	1.772	1.554	1.683	3.012	99.539	0.799	0.268	38.477
Cambodia	2.902	0.606	0.342	1.480	1.384	9.297	0.697	123.248	0.658	0.195	27.136
China	3.641	0.911	0.989	2.121	1.933	3.312	4.327	104.932	0.443	−0.284	150.172
Fiji	3.580	1.042	0.562	1.676	1.697	8.830	7.323	107.368	0.475	−0.161	119.363
India	3.119	1.023	1.478	1.765	1.632	1.888	3.807	109.952	0.846	0.134	70.787
Indonesia	3.494	0.997	0.975	1.577	1.456	1.812	5.815	107.473	0.804	0.113	40.734
Lao PDR	3.059	0.434	−0.307	1.532	1.446	4.718	0.870	117.358	0.522	0.166	12.541
Malaysia	3.966	1.059	0.849	2.020	2.007	3.514	3.272	101.286	0.342	0.254	127.496
Maldives	3.859	1.062	2.015	1.645	1.640	8.039	4.469	111.703	0.554	0.426	60.250
Mongolia	3.457	1.761	−0.137	1.573	1.599	10.898	5.005	108.165	0.892	0.188	39.862
Myanmar	2.969	0.299	0.033	1.016	0.664	3.258	0.802	100.615	0.856	−0.098	25.590
Nepal	2.768	0.712	0.791	1.735	1.584	0.241	2.222	132.629	0.889	0.059	62.737
Pakistan	3.022	0.942	1.091	1.502	1.320	1.495	4.016	91.011	0.921	0.315	46.790
Philippines	3.334	0.904	1.218	1.570	1.323	1.468	3.535	107.830	0.576	0.225	52.027
Samoa	3.549	1.334	0.943	1.581	1.641	2.200	5.323	107.628	0.444	−0.144	58.564
Solomon Islands	3.119	0.736	−0.215	1.433	1.256	7.667	2.078	111.243	0.803	0.353	26.806
Sri Lanka	3.450	1.161	1.540	1.585	1.528	1.278	5.415	99.394	0.828	−0.129	50.900
Thailand	3.701	1.026	1.050	1.839	1.825	2.807	0.954	99.681	0.392	−0.315	139.118
Timor-Leste	2.871	0.454	0.062	1.494	1.142	2.816	4.708	112.441	0.540	0.311	−18.676
Tonga	3.549	1.339	1.293	1.618	1.540	3.318	1.464	109.798	0.568	−0.274	37.943
Vanuatu	3.455	1.314	0.386	1.898	1.752	6.191	5.434	117.376	0.258	0.372	57.779
Vietnam	3.114	0.547	0.888	2.020	1.986	5.859	2.226	103.687	0.836	0.010	101.120
Total	3.287	0.912	0.727	1.636	1.505	3.954	3.723	108.111	0.663	0.107	57.611

Furthermore, the mean value of 3.954 with a standard deviation of 5.312 for FDI_i,t explains that there is a huge difference between these Asian countries in their proportion of net FDI inflows to the GDP ratio. Mongolia has the highest proportion of net inflows to GDP with a mean value of 10.898 and Bangladesh has the lowest with a mean value of 1.116. The mean value of 3.723 for UMP_i,t explains that the unemployment rate is quite high in these Asian countries. Furthermore, primary school enrollment rate is highest in Nepal with a mean value of 132.629 and lowest in Pakistan with a mean value of 91.011 for EDU_i,t. The mean value of 0.663 with a maximum value of 1.544 for LINF_i,t explains that these countries have experienced a quite high inflation rate during 2004 to 2016. Furthermore, most of these Asian countries have positive population growth except China (–0.284), Fiji (–0.161), Myanmar (–0.098), Samoa (–0.144), Sri Lanka (–0.129), Thailand (–0.315), and Tonga (–0.274). Moreover, the mean value of 57.611 with a standard deviation of 43.789 for DCPFS_i,t explains that there is a huge difference between these Asian countries in their ratio of domestic credit provided by their financial sector. The financial sector of China provides the highest amount of domestic credit with a mean value of 150.172 and this value is the lowest for Afghanistan (0.133).

Correlation Statistics and Robustness Tests

Table 4 presents the pairwise correlation matrix for dependent and all the explanatory variables. We find a significant correlation greater than 0.50 between financial permeation variables (LogFP1_i,t, LogFP2_i,t, LogFP3_i,t, and LogFP4_i,t). However, this high correlation is not a problem because we use four different regression models and include only one proxy of financial permeation in one model (see Tables 8 and 9). Furthermore, we find a high correlation between DCPFS_i,t and LogFP3_i,t as well as LogFP4_i,t. Therefore, we find out variance inflation factor (VIF) to check for multicollinearity issue. However, we do not find VIF greater than 10 (see Table 4), and therefore multicollinearity is not a problem for our regression models (Ott & Longnecker, 2015). Furthermore, we check for heteroscedasticity by applying modified Wald test and check for autocorrelation by applying Wooldridge test. We use clustered robust standard errors to control for heteroscedasticity and serial correlation in the fixed effects regression model. Moreover, we use 1-year lag of independent variables to control for possible endogeneity that may arise from simultaneity between economic growth and financial permeation, following previous studies (Lin, 2014; Rugman, Li, & Hoon Oh, 2009; Xie & Li, 2013). Moreover, we also run country and time fixed effects regression model to validate the results of our study.

Table 4.

Correlation Matrix.

Variables	LogGDPC_i,t	LogFP1_i,t	LogFP2_i,t	LogFP3_i,t	LogFP4_i,t	FDI_i,t	UMP_i,t	EDU_i,t	LINF_i,t	LPOPG_i,t	DCPFS_i,t	VIF
LogGDPC_i,t	1.0000
LogFP1_i,t	.6588*	1.0000										1.17
LogFP2_i,t	.4122*	.4086*	1.0000									1.29
LogFP3_i,t	.5251*	.4414*	.4238*	1.0000								2.18
LogFP4_i,t	.6147*	.5559*	.4683*	.9038*	1.0000							2.34
FDI_i,t	.1276*	.1146*	−.1620*	.0364	.1122*	1.0000						1.08
UMP_i,t	.0766	.1313*	.0010	−.0529	−.0603	−.0513	1.0000					1.14
EDU_i,t	−.2176*	−.0733	−.1181*	.0327	.0250	.1956*	−.1827*	1.0000				1.18
LINF_i,t	−.3512*	−.1297*	−.0437	−.2592*	−.2178*	.0672	−.0212	−.0213	1.0000			1.10
LPOPG_i,t	−.2978*	−.1937*	−.2914*	−.2291*	−.3047*	.0854	.2432*	.1147*	.0946	1.0000		1.45
DCPFS_i,t	.6158*	.3163*	.4326*	.6993*	.7303*	−.0033	−.0022	−.1950*	−.2695*	−.4859	1.0000	2.93

Note. VIF = variance inflation factor.

Significance level at 5%.

Cross-Sectional Dependence (CD) Test

When employing panel data into analysis, it is essential to inspect the existence of CD in the series. Overlooking CD determination may yield influenced and inconsistent outcomes. Pesaran (2004) stated that the CD test is a common test relatable to a great array of panel data models, containing stationary and non-stationary dynamic heterogeneous panel. We perform the CD test of the Lagrange multiplier (LM) test by Breusch and Pagan (1980) and the CD test recommended by Pesaran (2004) to examine whether CD exists in our model. Breusch and Pagan (1980) recommended LM statistic, which is usable for fixed N as T goes to infinity. Under the null hypothesis of no cross-sectional dependence, e_i,t is anticipated to be independent and distributed identically over time and across cross-sectional units. Under the alternative, e_i,t might be correlated across cross-sections but the assumption of no serial correlation remains. LM statistic is calculated as follows:

LM = T \sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} {\hat{ρ}}_{i j}^{2}),

(3)

where ${\hat{ρ}}_{i j}$ indicates the sample estimation of the pairwise correlation of residuals. LM is asymptotically distributed as chi-square with N (N – 1)/2 degrees of freedom under the null hypothesis of no CD.

Pesaran (2004) suggested the CD statistic as follows:

CD = \sqrt{\frac{2 T}{N (N - 1)}} (\sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} {\hat{ρ}}_{i j}),

(4)

where T reflects the time period, N is the number of the cross-sections, and ${\hat{ρ}}_{i j}$ indicates the correlation between the ith and jth error terms. Also, unlike LM statistic, CD statistics has zero mean for the fixed values of T and N. In Equation 4,

{\hat{ρ}}_{i j} = \sum_{t = 1}^{T} \frac{ℓ_{i, t} ℓ_{j t}}{^{{(\sum_{t = 1}^{T} ℓ_{i, t}^{2})}^{1 / 2} {(\sum_{t = 1}^{T} ℓ_{j t}^{2})}^{1 / 2}}},

(5)

where $ℓ_{i, t}$ describes the ordinary least squares (OLS) error terms on T observations for each i = 1, . . ., N. Table 5 displays the findings of CD tests. Based on the findings of CD tests, we reject the null hypothesis of no CD.

Table 5.

Results for CD Test.

CD test	CD statistic	p value
CD_LM (Breusch & Pagan, 1980)	118.357	.000
CD_LM1 (Pesaran, 2004)	11.638	.013
CD_LM2 (Pesaran, 2004)	−2.013	.000

Note. CD = cross-sectional dependence.

Panel Unit Root Test

From the results of the CD test shown in Table 5, we found that there exists CD among the series. Hence, we conducted second-generation unit root tests in preference to first generation because second-generation panel unit root tests illustrates the CD among the cross-sectional units of the panel, whereas the first-generation one considers the independency of all the cross-sections. To tackle the cross-section dependence issue, Pesaran (2007) recommended cross-sectional augmented Dickey Fuller (CADF) test statistic by adding cross-section average of lagged levels and first differences of the specific series to traditional or augmented Dickey Fuller test. In other words, the CADF test is an extension of the Cross-Sectional Augmented IPS (CIPS) test of Im, Pesaran, and Shin (2003):

\begin{array}{l} Δ y_{i, t} = c_{i} + α_{i} y_{i, t - 1} + β_{i} {\bar{y}}_{t - 1} + \sum_{j = 0}^{p} γ_{i j} Δ {\bar{y}}_{t - j} + \sum_{j = 1}^{p} δ_{i j} Δ y_{i, t - j} \\ + ε_{i, t} (i = 1, \dots, n), \end{array}

(6)

where $c_{i}$ indicates a deterministic term, ${\bar{y}}_{t}$ signifies the cross-sectional mean at time t, and p designates the lag order. Let the t-ratio of $α_{i}$ be signified by $t_{i} (N, T)$ . The average of the t-ratios, symbolized by CIPS, is calculated as

CIPS (N, T) = N^{- 1} (\sum_{i = 1}^{N - 1} t_{i} (N, T)

(7)

Taking into account of the CD, Table 6 shows the results of Pesaran (2007) panel unit root test. As shown in Table 6, we reject the null hypothesis of unit root for all the variables in both intercept and intercept-trend models at the 1% significance level and indicate that all variables are stationary. Thus, it is suitable to employ regression on stationary dataset.

Table 6.

Results of Pesaran Unit Root Test (CIPS statistics).

	Intercept				Intercept and trend
Variable	Stat.	CV at 1%	CV at 5%	CV at 10%	Stat.	CV at 1%	CV at 5%	CV at 10%
LogGDPC_i,t	−3.17	−2.20	−2.10	−2.03	−3.26	−2.20	−2.10	−2.03
LogFP1_i,t	−2.82	−2.20	−2.10	−2.03	−2.93	−2.20	−2.10	−2.03
LogFP2_i,t	−2.86	−2.20	−2.10	−2.03	−3.05	−2.20	−2.10	−2.03
LogFP3_i,t	−2.79	−2.20	−2.10	−2.03	−2.90	−2.20	−2.10	−2.03
LogFP4_i,t	−2.75	−2.20	−2.10	−2.03	−2.87	−2.20	−2.10	−2.03
FDI_i,t	−2.54	−2.20	−2.10	−2.03	−2.66	−2.20	−2.10	−2.03
UMP_i,t	−2.63	−2.20	−2.10	−2.03	−2.72	−2.20	−2.10	−2.03
EDU_i,t	−3.29	−2.20	−2.10	−2.03	−3.41	−2.20	−2.10	−2.03
LINF_i,t	−3.02	−2.20	−2.10	−2.03	−3.13	−2.20	−2.10	−2.03
LPOPG_i,t	−3.39	−2.20	−2.10	−2.03	−2.46	−2.20	−2.10	−2.03
DCPFS_i,t	−2.65	−2.20	−2.10	−2.03	−2.74	−2.20	−2.10	−2.03

Note. CVs are obtained from Pesaran (2006). CIPS = Cross-Sectional Augmented IPS; CV = critical value.

Granger Non-causality Test

We report the results of Granger non-causality test for each of the variables in Table 7 to find out causality between dependent and independent variables. We choose Dumitrescu and Hurlin (2012) Granger non-causality test because it assumes that all the coefficients are different across all cross-sections and we have 24 different countries. The optimal lag was determined to be 1 by Akaike’s information criterion (AIC). Focusing on the causalities between LogGDPC_i,t and different financial permeation proxies, we find that LogFP2_i,t and LogFP3_i,t cause LogGDPC_i,t at 1%. Whereas LogFP1_i,t causes LogGDPC_i,t at 1% according to W-statistic and Z-bar statistic but does not cause according to Z-bar tilde statistic, LogFP4_i,t causes LogGDPC_i,t at 1% according to W-statistic and Z-bar statistic at 10% according to Z-bar tilde statistic. The results also explain that LogGDPC_i,t causes LogFP1_i,t, LogFP2_i,t, LogFP3_i,t, and LogFP4_i,t at 1%. Thus, we find that there exists reverse causality between financial permeation and economic growth consistent with the findings of Evans and Lawanson (2017), Gourène and Mendy (2017), and Sharma (2016). Focusing on the causalities between LogGDPC_i,t and control variables, we find that EDU_i,t and LINF_i,t cause LogGDPC_i,t at 1% and 5%. DCPFS_i,t and FDI_i,t cause LogGDPC_i,t according to W-statistic and Z-bar statistic but does not cause according to Z-bar tilde statistic. Findings indicate that LogGDPC_i,t also causes EDU_i,t, LINF_i,t, DCPFS_i,t, and FDI_i,t. Thus, there is bidirectional causality between per capita GDP and education, per capita GDP and inflation, per capita GDP and domestic credit provided by the financial sector, and per capita GDP and FDI. Findings shown in Table 7 indicate that, consistent with Kim et al. (2018), there is unidirectional causality from population growth to per capita GDP. We find that UMP_i,t does not cause LogGDPC_i,t, whereas LogGDPC_i,t causes unemployment. That is, there is unidirectional causality from per capita GDP to unemployment rate which is consistent with Bist (2018). Therefore, according to these results, we find evidence for the overall causality between financial permeation, economic openness factors, and economic growth in 24 Asian countries.

Table 7.

Results for Dumitrescu and Hurlin (2012) Panel Granger Causality.

Null hypothesis	W Stat.	Z-bar	p value	Z-bar tilde	p value
LogFP1_i,t does not homogeneously cause LogGDPC_i,t	2.008	3.494	.000***	1.539	.123
LogFP2_i,t does not homogeneously cause LogGDPC_i,t	2.635	5.662	.000***	2.873	.004***
LogFP3_i,t does not homogeneously cause LogGDPC_i,t	3.970	10.289	.000***	5.718	.000***
LogFP4_i,t does not homogeneously cause LogGDPC_i,t	2.196	4.144	.000***	1.939	.052*
FDI_i,t does not homogeneously cause LogGDPC_i,t	2.032	3.575	.000***	1.590	.111
UMP_i,t does not homogeneously cause LogGDPC_i,t	1.120	0.415	.677	−0.352	.724
EDU_i,t does not homogeneously cause LogGDPC_i,t	2.296	4.489	.000***	2.152	.031**
LINF_i,t does not homogeneously cause LogGDPC_i,t	4.215	11.136	.000***	6.238	.000***
LPOPG_i,t does not homogeneously cause LogGDPC_i,t	2.482	5.134	.000***	2.548	.010**
DCPFS_i,t does not homogeneously cause LogGDPC_i,t	1.648	2.244	.024**	0.771	.440
LogGDPC_i,t does not homogeneously cause LogFP1_i,t	3.846	9.858	.000***	5.453	.000***
LogGDPC_i,t does not homogeneously cause LogFP2_i,t	3.923	10.126	.000***	5.617	.000***
LogGDPC_i,t does not homogeneously cause LogFP3_i,t	2.382	4.789	.000***	2.336	.019**
LogGDPC_i,t does not homogeneously cause LogFP4_i,t	4.107	10.763	.000***	6.010	.000***
LogGDPC_i,t does not homogeneously cause FDI_i,t	2.603	5.554	.000***	2.806	.005***
LogGDPC_i,t does not homogeneously cause UMP_i,t	4.472	12.027	.000***	6.787	.000***
LogGDPC_i,t does not homogeneously cause EDU_i,t	6.090	17.632	.000***	10.233	.000***
LogGDPC_i,t does not homogeneously cause LINF_i,t	2.313	4.547	.000***	2.187	.028**
LogGDPC_i,t does not homogeneously cause LPOPG_i,t	4.545	2.429	.015**	0.885	.376
LogGDPC_i,t does not homogeneously cause DCPFS_i,t	5.297	12.280	.000***	6.942	.000***

Note. This table presents the results of Dumitrescu and Hurlin (2012) Granger non-causality test for one lag (k = 1) on level for all the variables.

Statistical significance at the 10% level. **Statistical significance at the 5% level. ***Statistical significance at the 1% level.

Regression Analysis

The results of Dumitrescu and Hurlin (2012) Granger non-causality test (see Table 7) explain that our model contains reverse causality or simultaneity. To counter this reverse causality or simultaneity that may cause endogeneity, we use 1-year lag of independent variables in our regression model following the studies of Lin (2014), Rugman et al. (2009), and Xie and Li (2013). The optimal number of lags was 1 (k = 1) selected using AIC. Table 8 presents the results of country and time fixed effects regression techniques for four regression models, separately. Based on the findings of Hausman (1978), we conducted the fixed effects regression analysis. The fixed effects regression explains 47% to 58% of the variations in economic growth of Asian countries, whereas time fixed effects regression explains 50% to 67% of the variations.

Table 8.

Regression Analysis.

	Country fixed effects				Time fixed effects
	(1)	(2)	(3)	(4)	(1)	(2)	(3)	(4)
FDI_{i,t – 1}	0.002** (0.001)	0.002** (0.001)	0.001 (0.001)	0.001 (0.001)	0.006** (0.003)	0.012*** (0.003)	0.018*** (0.003)	0.014*** (0.003)
UMP_{i,t – 1}	−0.007* (0.003)	−0.005 (0.003)	−0.010** (0.004)	−0.012*** (0.004)	−0.001 (0.005)	0.012** (0.005)	0.008 (0.005)	0.011 (0.005)
EDU_{i,t – 1}	0.002* (0.001)	0.002 (0.001)	0.003*** (0.001)	0.003*** (0.001)	−0.004*** (0.002)	−0.007*** (0.002)	−0.005*** (0.002)	−0.006*** (0.002)
LINF_{i,t – 1}	−0.021** (0.010)	−0.019* (0.010)	−0.013 (0.009)	−0.027*** (0.009)	−0.212*** (0.039)	−0.203*** (0.051)	−0.236*** (0.050)	−0.198*** (0.052)
LPOPG_{i,t – 1}	−0.182** (0.088)	−0.170** (0.082)	−0.119 (0.087)	−0.132 (0.083)	0.014 (0.065)	−0.121 (0.074)	−0.053 (0.072)	−0.134 (0.085)
DCPFS_{i,t – 1}	0.002*** (0.001)	0.002*** (0.001)	0.002*** (0.000)	0.002*** (0.001)	0.003*** (0.000)	0.002*** (0.001)	0.003*** (0.000)	0.003*** (0.001)
LogFP1_{i,t – 1}	0.206*** (0.055)				0.449*** (0.025)
LogFP2_{i,t – 1}		0.220*** (0.041)					0.131*** (0.030)
LogFP3_{i,t – 1}			0.302*** (0.032)					0.295*** (0.065)
LogFP4_{i,t – 1}				0.156*** (0.051)		0.365*** (0.049)
Constant	2.822*** (0.115)	2.901*** (0.113)	2.464*** (0.073)	2.684*** (0.119)	3.296*** (0.185)	3.462*** (0.186)	3.640*** (0.196)	3.392*** (0.185)
Observations	288	288	288	288	288	288	288	288
No. of countries	24	24	24	24	24	24	24	24
Adjusted R²	.501	.543	.583	.471	.671	.536	.525	.501
F-statistics	14.779***	15.439***	43.689***	12.952***	62.717***	39.343***	33.274***	31.751***
Hausman test	59.13***	62.46***	86.17***	54.16***	91.83***	73.18***	64.13***	57.17***

Note. This table represents the regression results for country and time fixed effects analysis for four models. Model 1 includes LogGDPC_i,t, LogFP1_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, and DCPFS_{i,t – 1}. Model 2 includes LogGDPC_i,t, LogFP2_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, and DCPFS_{i,t – 1}. Model 3 includes LogGDPC_i,t, LogFP3_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, and DCPFS_{i,t – 1}. Model 4 includes LogGDPC_i,t, LogFP4_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, and DCPFS_{i,t – 1}. Standard errors are in parenthesis.

p < .1. **p < .05. ***p < .01.

Furthermore, we find a significant positive relationship between economic growth and all four proxies of financial permeation at 1% after controlling for country as well as time fixed effects. These results explain that an increase in the number of commercial bank branches per 100,000 adults, number of commercial bank branches per 1,000 km², outstanding deposits with commercial banks to GDP, and outstanding loans from commercial banks to GDP stimulates economic growth, irrespective of country as well as time. Our estimated findings are consistent with the findings of Inoue and Hamori (2016) and Kim et al. (2018). We also find a positive relationship between share of net FDI to GDP and economic growth for Models 1 and 2 of country fixed effects regression and all four models of time fixed effects regression. This positive relationship, which is similar to the findings of Solarin and Dahalan (2014), explains that an increase in FDI increases economic growth in Asian countries. Furthermore, we find a negative relationship between unemployment rate and economic growth for Models 1, 3, and 4 of country fixed effects as well as Models 1 and 2 of time fixed effects regression. This negative relationship explains that an increase in unemployment rate decreases economic growth of Asian countries. Furthermore, we find a negative relationship between inflation rate and economic growth for Models 1, 2, and 4 of country fixed effects regression and all four models of time fixed effects regression. We also find a negative relationship between population growth and economic growth but only for Models 1 and 2 of country fixed effects regression at 5% significance. These negative relationships explain that an increase in inflation rate and population growth decreases economic growth in Asian countries. Moreover, we also find a significant positive relationship between domestic credits provided by the financial sector and economic growth for all four models of country fixed effects as well as time fixed effects regression. This positive relationship explains that the developed financial institutions also stimulate economic growth.

Table 9 presents the results of country fixed effects regression techniques for Equation 2. We include a time dummy (GFC_t) interaction variable for 2007 and 2008 to control for the impact of global financial crisis.

Table 9.

Country Fixed Effects Regression Analysis With Global Financial Crisis.

	(1)	(2)	(3)	(4)
FDI_{i,t – 1}	0.002** (0.001)	0.002** (0.001)	0.001 (0.001)	0.002* (0.001)
UMP_{i,t – 1}	−0.007* (0.003)	−0.006 (0.004)	−0.010** (0.004)	−0.012*** (0.004)
EDU_{i,t – 1}	0.002* (0.001)	0.002 (0.001)	0.002*** (0.001)	0.003*** (0.001)
LINF_{i,t – 1}	−0.020** (0.010)	−0.019* (0.010)	−0.012 (0.009)	−0.026*** (0.009)
LPOPG_{i,t – 1}	−0.177* (0.086)	−0.167* (0.082)	−0.115 (0.085)	−0.125 (0.080)
DCPFS_{i,t – 1}	0.002*** (0.001)	0.002*** (0.001)	0.002*** (0.001)	0.002*** (0.001)
LogFP1_{i,t – 1} × GFC_t	−0.014* (0.007)
LogFP1_{i,t – 1}	0.204*** (0.054)
LogFP2_{i,t – 1} × GFC_t		−0.005 (0.006)
LogFP2_{i,t – 1}		0.219*** (0.041)
LogFP3_{i,t – 1} × GFC_t			−0.011** (0.005)
LogFP3_{i,t – 1}			0.298*** (0.032)
LogFP4_{i,t – 1} × GFC_t				−0.015** (0.006)
LogFP4_{i,t – 1}				0.160*** (0.050)
Constant	2.846*** (0.112)	2.906*** (0.112)	2.501*** (0.070)	2.718*** (0.113)
Observations	288	288	288	288
No. of countries	24	24	24	24
Adjusted R²	.506	.543	.592	.485
F-statistics	13.650***	13.537***	39.877***	11.743***
Hausman test	53.57***	51.21***	72.79***	48.42***

Note. This table represents the regression results for country fixed effects analysis for four models controlling for global financial crisis (2007-2008). Model 1 includes LogGDPC_i,t, LogFP1_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, DCPFS_{i,t – 1}, and LogFP1_{i,t – 1} × GFC_t;. Model 2 includes LogGDPC_i,t, LogFP2_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, DCPFS_{i,t – 1}, and LogFP2_{i,t – 1} × GFC_t. Model 3 includes LogGDPC_i,t, LogFP3_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, DCPFS_{i,t – 1}, and LogFP3_{i,t – 1} × GFC_t. Model 4 includes LogGDPC_i,t, LogFP4_{i,t – 1}, FDI_{i,t – 1}, UMP_{i,t – 1}, EDU_{i,t – 1}, LINF_{i,t – 1}, LPOPG_{i,t – 1}, DCPFS_{i,t – 1}, and LogFP1_{i,t – 4} × GFC_t. Standard errors are in parenthesis.

p < .1. **p < .05. ***p < .01.

We find significant results of this dummy interaction variable for Models 1, 3, and 4, explaining the negative impact of financial crisis on economic growth of Asian countries which is similar to the results of Asteriou and Spanos (2019). However, the impact of financial permeation on economic growth remains positive and significant. These positive/negative relationships explain that although global financial crisis reduces the positive impact of financial permeation on economic growth of Asian countries, its impact was not very much significant. The plausible reason for this less significant negative impact may be that the origin of global financial crisis was the United States and its foremost impact was in the United States and Europe. Asian countries also faced its consequences but the impact was very low as compared with the United States and European countries.

Conclusion

Asian countries are the fast-growing developing economies as compared with the other economies in the world. Although the level of financial permeation in Asian countries is low, governments of these countries have articulated and adopted financial inclusion policies to attain inclusive economic growth and as such the level of financial permeation is also growing in these economies. Using the panel data of 24 Asian countries for the period from 2004 to 2016, this research empirically explored whether financial permeation contributes to economic growth of the Asian region. Findings show that access to finance indicators and usage indicators have significant positive effects on economic growth of Asian countries. Therefore, we argue that financial permeation promotes economic growth of Asian economies. Accordingly, in terms of policy implications, we recommend that Asian countries’ governments should develop and implement supportive policies to magnify financial permeation through commercial banks as a way of achieving sustainable economic growth. For instance, they can assist banks to increase the level of penetration and eradicate access blockades while maintaining banks’ profitability. These include a reduction in cost of financial transactions and ensuring conducive environment to the financial system. Policymakers can make policies to connect with financial technology providers, FinTechs, to provide digital financial services which will allow banks to offer low-cost products and services to all including individuals in remote areas, which in turn will raise the level of financial permeation. We also detected that schooling has significant positive impacts, whereas unemployment has negative effects on economic growth. Thus, we contend that, to accomplish sustainable growth, governments and policymakers of Asian nations should focus on the proliferation of education as well as employment level of the countries.

The key implication of this research is that it will generate notable attention of governments and policymakers of the Asian region to carry out such plans and policies to form more facilitating atmosphere for financial inclusion, which eventually will stimulate inclusive and sustainable economic growth of countries. Although this research made a wide-ranging exertion on inspecting whether financial permeation promotes economic growth, the main limitation of this study is that we have used 12- to 13-year dataset which is not enough to make a decision in the long run. Also, data scarceness on other indicators of financial permeation such as ATMs per 1,000 km², data relating to Microfinance Institutions (MFIs), was a gigantic issue. Therefore, upon availability of data, future research might include these measures of financial permeation which will make the findings more inclusive and robust. Our study focused only on the association of financial permeation on economic growth which calls for further investigation on regulatory frameworks and financial technologies that could foster financial permeation and make the economic growth more sustainable.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Md. Nur Alam Siddik

Notes

Author Biographies

Md. Nur Alam Siddik obtained his BBA degree and MBA degree in Finance and Banking from University of Rajshahi, Bangladesh and obtained PhD degree in Finance from Dongbei University of Finance and Economics, Dalian, China. At present, he is an associate professor in the department of Finance and Banking, Begum Rokeya University, Rangpur, Bangladesh. His current research interests include financial inclusion, fintech, economic development, sustainable development.

Tanveer Ahsan is an assistant professor at Rennes School of Business, Rennes, France. He completed his PhD from School of Accounting, Dongbei University of Finance and Economics, Liaoning, Dalian, P.R. China in 2016. His research interests lie in corporate financing policies and panel data techniques.

Sajal Kabiraj,PhD is principal lecturer in Strategy and International Business at Häme University of Applied Sciences Ltd. (HAMK), Finland. He was earlier a tenured full professor with Dongbei University of Finance and Economics, Dalian, P.R. China from 2010 to 2019. He is the recipient of awards for teaching and research excellence in 2018, 2014, 2011 and 2008 and is a doctoral supervisor.

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